Since computers have become a common part of most office environments, the collection of electronic data stored on computer systems has become a primary focus in litigation, regulatory and/or law enforcement evidence discovery. As litigants and regulatory agencies have increased their focus of evidence discovery on data stored in computer systems, the amount of resources applied to electronic evidence data collection has exponentially increased. Accordingly, the discovery process of identifying, locating, collecting and reviewing voluminous amounts of potentially relevant data in both client and opposing party systems has become an increasingly difficult task.
Currently known methods of electronic evidence data discovery involve a process where one or more individuals manually collect electronic evidence data directly from the computing devices storing the data. The known methods are difficult because the operators collecting the evidence data (the data collectors) typically have to be physically located at a computing device or a computer network having a central server storing the electronic evidence data. While such existing practices are generally effective in collecting small quantities of electronic evidence data from a small-scale computer system, there are several disadvantages. In particular, the manual process of collecting evidence data from a large number of computing devices in a sizeable company requires a vast amount of resources that often results in an inefficient, time consuming process. More specifically, the manual process requires the data collectors to commute to the location of the computing devices and transport supporting equipment necessary to facilitate the evidence data collection. In addition, the manual process of data collection creates other resource problems as the data collectors typically disrupt the users of the computing devices during the data collection process.
The above-described difficulties are exasperated by the fact that the manual process of electronic evidence data collection also requires a large assortment of computer equipment to facilitate the data collection. In large computer network systems, there may be a many different types of computing devices that require different types of data retrieval equipment, such as specific types of parallel-port tape drives, floptical drives, etc. Having this need for a wide variety of data capture equipment creates the possibility of hardware compatibility issues, and in some situations, the hardware compatibility issues may prevent one from collecting data from some computing devices. In addition, manual data collectors, being human, may overlook or misidentify potentially relevant data and/or may apply differing data identification and/or data capture standards, thereby resulting in an inconsistent and/or incomplete set of potentially relevant data.
In addition to the resource and efficiency issues described above, the known methods of electronic evidence data collection present many other logistical and security issues. For instance, data collectors also have the difficult task of managing computer network login information to access the various computers storing the electronic evidence data. This task often creates many barriers for the data collectors as login and password information is often changed or miscommunicated. In addition, the communication of such security information such as a user's login and password often compromises the security of the computer system storing the electronic evidence data.
Accordingly, from the foregoing, there is a need for a system and method for automatically locating, identifying, and collecting relevant electronic evidence data stored in a plurality of remote computers. In addition, there is a need for a method and system for providing an electronic evidence data collection system that does not disrupt a user of the computing device storing the electronic evidence data.